



import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
				import textwrap

				from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
				import torch

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__snake_case     :Optional[Any]			       =False


__snake_case     :str			       =logging.get_logger(__name__)
__snake_case     :Union[str, Any]			       ='ybelkada/fonts'






def  lowerCamelCase_	(      )       ->							Tuple:

							'''simple docstring'''






							if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
														raise ImportError(
														    F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
														    'Pix2StructImageProcessor. Please upgrade torch.'				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Dict				)       ->							Tuple:

							'''simple docstring'''






							requires_backends(lowerCAmelCase__			,	['torch']				)
							_check_torch_version()

							A      						=    image_tensor.unsqueeze(0				)
							A      						=    torch.nn.functional.unfold(lowerCAmelCase__			,	(patch_height, patch_width)			,	stride=(patch_height, patch_width)				)
							A      						=    patches.reshape(image_tensor.size(0				)			,	image_tensor.size(1				)			,	lowerCAmelCase__			,	lowerCAmelCase__			,	-1				)
							A      						=    patches.permute(0			,	4			,	2			,	3			,	1				).reshape(
							    image_tensor.size(2				) // patch_height			,	image_tensor.size(3				) // patch_width			,	image_tensor.size(1				) * patch_height * patch_width			,	)
							return patches.unsqueeze(0				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	int = 36			,	lowerCAmelCase__	:	str = "black"			,	lowerCAmelCase__	:	str = "white"			,	lowerCAmelCase__	:	int = 5			,	lowerCAmelCase__	:	int = 5			,	lowerCAmelCase__	:	int = 5			,	lowerCAmelCase__	:	int = 5			,	lowerCAmelCase__	:	Optional[bytes] = None			,	lowerCAmelCase__	:	Optional[str] = None			,	)       ->							Image.Image:

							'''simple docstring'''






							requires_backends(lowerCAmelCase__			,	'vision'				)
							# Add new lines so that each line is no more than 80 characters.

							A      						=    textwrap.TextWrapper(width=80				)
							A      						=    wrapper.wrap(text=lowerCAmelCase__				)
							A      						=    '\n'.join(lowerCAmelCase__				)

							if font_bytes is not None and font_path is None:
														A      						=    io.BytesIO(lowerCAmelCase__				)
							elif font_path is not None:
														A      						=    font_path
							else:
														A      						=    hf_hub_download(lowerCAmelCase__			,	'Arial.TTF'				)
							A      						=    ImageFont.truetype(lowerCAmelCase__			,	encoding='UTF-8'			,	size=lowerCAmelCase__				)

							# Use a temporary canvas to determine the width and height in pixels when
							# rendering the text.
							A      						=    ImageDraw.Draw(Image.new('RGB'			,	(1, 1)			,	lowerCAmelCase__				)				)
							A						,   A						,   A						,   A      						=    temp_draw.textbbox((0, 0)			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							# Create the actual image with a bit of padding around the text.
							A      						=    text_width + left_padding + right_padding
							A      						=    text_height + top_padding + bottom_padding
							A      						=    Image.new('RGB'			,	(image_width, image_height)			,	lowerCAmelCase__				)
							A      						=    ImageDraw.Draw(lowerCAmelCase__				)
							draw.text(xy=(left_padding, top_padding)			,	text=lowerCAmelCase__			,	fill=lowerCAmelCase__			,	font=lowerCAmelCase__				)
							return image






def  lowerCamelCase_	(      lowerCAmelCase__	:	np.ndarray			,	lowerCAmelCase__	:	str			,	**lowerCAmelCase__	:	Tuple				)       ->							Optional[int]:

							'''simple docstring'''






							requires_backends(lowerCAmelCase__			,	'vision'				)

							# Convert to PIL image if necessary
							A      						=    to_pil_image(lowerCAmelCase__				)

							A      						=    render_text(lowerCAmelCase__			,	**lowerCAmelCase__				)
							A      						=    max(header_image.width			,	image.width				)

							A      						=    int(image.height * (new_width / image.width)				)
							A      						=    int(header_image.height * (new_width / header_image.width)				)

							A      						=    Image.new('RGB'			,	(new_width, new_height + new_header_height)			,	'white'				)
							new_image.paste(header_image.resize((new_width, new_header_height)				)			,	(0, 0)				)
							new_image.paste(image.resize((new_width, new_height)				)			,	(0, new_header_height)				)

							# Convert back to the original framework if necessary
							A      						=    to_numpy_array(lowerCAmelCase__				)

							if infer_channel_dimension_format(lowerCAmelCase__				) == ChannelDimension.LAST:
														A      						=    to_channel_dimension_format(lowerCAmelCase__			,	ChannelDimension.LAST				)

							return new_image



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     ['flattened_patches']


				def __init__(							self      :  Union[str, Any]						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  int = 2_048						, __UpperCamelCase      :  bool = False						, **__UpperCamelCase      :  int						, )		->     None:
											super().__init__(**__UpperCamelCase       )
											A      						=    patch_size if patch_size is not None else {'height': 16, 'width': 16}
											A      						=    do_normalize
											A      						=    do_convert_rgb
											A      						=    max_patches
											A      						=    is_vqa


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  np.ndarray						, __UpperCamelCase      :  int						, __UpperCamelCase      :  dict						, **__UpperCamelCase      :  int       )		->     np.ndarray:
											requires_backends(self.extract_flattened_patches						, 'torch'       )
											_check_torch_version()

											# convert to torch
											A      						=    to_channel_dimension_format(__UpperCamelCase						, ChannelDimension.FIRST       )
											A      						=    torch.from_numpy(__UpperCamelCase       )

											A						,   A      						=    patch_size['height'], patch_size['width']
											A						,   A      						=    get_image_size(__UpperCamelCase       )

											# maximize scale s.t.
											A      						=    math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width)       )
											A      						=    max(min(math.floor(scale * image_height / patch_height       )						, __UpperCamelCase       )						, 1       )
											A      						=    max(min(math.floor(scale * image_width / patch_width       )						, __UpperCamelCase       )						, 1       )
											A      						=    max(num_feasible_rows * patch_height						, 1       )
											A      						=    max(num_feasible_cols * patch_width						, 1       )

											A      						=    torch.nn.functional.interpolate(
											    image.unsqueeze(0       )						, size=(resized_height, resized_width)						, mode='bilinear'						, align_corners=__UpperCamelCase						, antialias=__UpperCamelCase						, ).squeeze(0       )

											# [1, rows, columns, patch_height * patch_width * image_channels]
											A      						=    torch_extract_patches(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )

											A      						=    patches.shape
											A      						=    patches_shape[1]
											A      						=    patches_shape[2]
											A      						=    patches_shape[3]

											# [rows * columns, patch_height * patch_width * image_channels]
											A      						=    patches.reshape([rows * columns, depth]       )

											# [rows * columns, 1]
											A      						=    torch.arange(__UpperCamelCase       ).reshape([rows, 1]       ).repeat(1						, __UpperCamelCase       ).reshape([rows * columns, 1]       )
											A      						=    torch.arange(__UpperCamelCase       ).reshape([1, columns]       ).repeat(__UpperCamelCase						, 1       ).reshape([rows * columns, 1]       )

											# Offset by 1 so the ids do not contain zeros, which represent padding.
											row_ids += 1
											col_ids += 1

											# Prepare additional patch features.
											# [rows * columns, 1]
											A      						=    row_ids.to(torch.floataa       )
											A      						=    col_ids.to(torch.floataa       )

											# [rows * columns, 2 + patch_height * patch_width * image_channels]
											A      						=    torch.cat([row_ids, col_ids, patches]						, -1       )

											# [max_patches, 2 + patch_height * patch_width * image_channels]
											A      						=    torch.nn.functional.pad(__UpperCamelCase						, [0, 0, 0, max_patches - (rows * columns)]       ).float()

											A      						=    to_numpy_array(__UpperCamelCase       )

											return result


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  np.ndarray						, __UpperCamelCase      :  Optional[Union[str, ChannelDimension]] = None						, **__UpperCamelCase      :  List[str]       )		->     np.ndarray:
											if image.dtype == np.uinta:
																		A      						=    image.astype(np.floataa       )

											# take mean across the whole `image`
											A      						=    np.mean(__UpperCamelCase       )
											A      						=    np.std(__UpperCamelCase       )
											A      						=    max(__UpperCamelCase						, 1.0 / math.sqrt(np.prod(image.shape       )       )       )

											return normalize(__UpperCamelCase						, mean=__UpperCamelCase						, std=__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  ImageInput						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Optional[Dict[str, int]] = None						, __UpperCamelCase      :  Optional[Union[str, TensorType]] = None						, __UpperCamelCase      :  ChannelDimension = ChannelDimension.FIRST						, **__UpperCamelCase      :  Dict						, )		->     ImageInput:
											A      						=    do_normalize if do_normalize is not None else self.do_normalize
											A      						=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
											A      						=    patch_size if patch_size is not None else self.patch_size
											A      						=    max_patches if max_patches is not None else self.max_patches
											A      						=    self.is_vqa

											if kwargs.get('data_format'						, __UpperCamelCase       ) is not None:
																		raise ValueError('data_format is not an accepted input as the outputs are '       )

											A      						=    make_list_of_images(__UpperCamelCase       )

											if not valid_images(__UpperCamelCase       ):
																		raise ValueError(
																		    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																		    'torch.Tensor, tf.Tensor or jax.ndarray.'       )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																		A      						=    [convert_to_rgb(__UpperCamelCase       ) for image in images]

											# All transformations expect numpy arrays.
											A      						=    [to_numpy_array(__UpperCamelCase       ) for image in images]

											if is_vqa:
																		if header_text is None:
																									raise ValueError('A header text must be provided for VQA models.'       )
																		A      						=    kwargs.pop('font_bytes'						, __UpperCamelCase       )
																		A      						=    kwargs.pop('font_path'						, __UpperCamelCase       )

																		if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																									A      						=    [header_text] * len(__UpperCamelCase       )

																		A      						=    [
																		    render_header(__UpperCamelCase						, header_text[i]						, font_bytes=__UpperCamelCase						, font_path=__UpperCamelCase       )
																		    for i, image in enumerate(__UpperCamelCase       )
																		]

											if do_normalize:
																		A      						=    [self.normalize(image=__UpperCamelCase       ) for image in images]

											# convert to torch tensor and permute
											A      						=    [
											    self.extract_flattened_patches(image=__UpperCamelCase						, max_patches=__UpperCamelCase						, patch_size=__UpperCamelCase       )
											    for image in images
											]

											# create attention mask in numpy
											A      						=    [(image.sum(axis=-1       ) != 0).astype(np.floataa       ) for image in images]

											A      						=    BatchFeature(
											    data={'flattened_patches': images, 'attention_mask': attention_masks}						, tensor_type=__UpperCamelCase       )

											return encoded_outputs



'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class        lowercase__    (		_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Dict       =    KandinskyVaaPipeline
							A_				: List[str]       =    [
							    """image_embeds""",
							    """negative_image_embeds""",
							]
							A_				: Optional[int]       =    ["""image_embeds""", """negative_image_embeds"""]
							A_				: Optional[Any]       =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							A_				: Dict       =    False






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim * 4






							@property
							def UpperCAmelCase_      (       self    ):
											return 100






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """in_channels""": 4,
											    # Out channels is double in channels because predicts mean and variance
											    """out_channels""": 8,
											    """addition_embed_type""": """image""",
											    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
											    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
											    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
											    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    """layers_per_block""": 1,
											    """encoder_hid_dim""": self.text_embedder_hidden_size,
											    """encoder_hid_dim_type""": """image_proj""",
											    """cross_attention_dim""": self.cross_attention_dim,
											    """attention_head_dim""": 4,
											    """resnet_time_scale_shift""": """scale_shift""",
											    """class_embed_type""": None,
											}

											_SCREAMING_SNAKE_CASE					:			Any							= UNetaDConditionModel(**__snake_case    )
											return model






							@property
							def UpperCAmelCase_      (       self    ):
											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= VQModel(**self.dummy_movq_kwargs    )
											return model






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.dummy_unet
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.dummy_movq

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= DDIMScheduler(
											    num_train_timesteps=1000       ,   beta_schedule="""linear"""       ,   beta_start=0.00085       ,   beta_end=0.012       ,   clip_sample=__snake_case       ,   set_alpha_to_one=__snake_case       ,   steps_offset=1       ,   prediction_type="""epsilon"""       ,   thresholding=__snake_case       ,   )

											_SCREAMING_SNAKE_CASE					:			Dict							= {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}
											return components






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=0    ):
											_SCREAMING_SNAKE_CASE					:			int							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(seed + 1    )    ).to(
											    __snake_case    )
											if str(__snake_case    ).startswith("""mps"""    ):
															_SCREAMING_SNAKE_CASE					:			str							= torch.manual_seed(__snake_case    )
											else:
															_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """generator""": generator,
											    """height""": 64,
											    """width""": 64,
											    """guidance_scale""": 4.0,
											    """num_inference_steps""": 2,
											    """output_type""": """np""",
											}
											return inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """cpu"""

											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_dummy_components()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.pipeline_class(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipe.to(__snake_case    )

											pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= pipe(**self.get_dummy_inputs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= output.images

											_SCREAMING_SNAKE_CASE					:			Dict							= pipe(
											    **self.get_dummy_inputs(__snake_case    )       ,   return_dict=__snake_case       ,   )[0]

											_SCREAMING_SNAKE_CASE					:			Tuple							= image[0, -3:, -3:, -1]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											_SCREAMING_SNAKE_CASE					:			Dict							= np.array(
											    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]    )

											assert (
											    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





@slow
@require_torch_gpu
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= KandinskyVaaPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-prior"""       ,   torch_dtype=torch.floataa    )
											pipe_prior.to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= KandinskyVaaPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-decoder"""       ,   torch_dtype=torch.floataa    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipeline.to(__snake_case    )
											pipeline.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Dict							= """red cat, 4k photo"""

											_SCREAMING_SNAKE_CASE					:			List[str]							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= pipe_prior(
											    __snake_case       ,   generator=__snake_case       ,   num_inference_steps=5       ,   negative_prompt=""""""       ,   ).to_tuple()

											_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= pipeline(
											    image_embeds=__snake_case       ,   negative_image_embeds=__snake_case       ,   generator=__snake_case       ,   num_inference_steps=100       ,   output_type="""np"""       ,   )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(__snake_case       ,   __snake_case    )




